Hi everyone,

Since I'm not sure whether my email with my questions regarding the
checking of data for outliers using GLMM slipped through the cracks
when I sent it earlier, I, again, ask for your help answering my
questions and solving my problems.

My somewhat original message: I have a question relating to the
checking for outliers in my dataset. I created a GLMER model using the
lme4 package that contains multiple fixed and random effects.

I'm investigating the detection rate (ValidDetections) of ultrasonic
receivers as a function of distance from transmitters, water depth and
temperature and wind speed. My model is specified as follows:

m <- lmer(ValidDetections ~ tm + sc.c.distance + tm:sc.c.distance +
c.tm.depth + c.receiver.depth + c.temp + c.wind + (1|replicate) +
(1|SUR.ID) + (1|Area) + (1|Day), data = df3, family = poisson)

where tm (denotes transmitter used) is a fixed effect with two levels,
sc.c.distance, c.tm.depth, c.receiver.depth, c.temp, c.wind are fixed
effects that are centered and scaled (latter only for distance), and
replicate, SUR.ID (receiver ID), Area and Day are random effects. My
data file can be regarded as a partially crossed design, with multiple
valid detections per receiver ID, multiple receivers per distance,
multiple replicates per distance, multiple areas per distances, and
multiple days per area.

I was guided to use a package called influence.ME that could check for
both influential points as well as outliers, but unfortunately this
package can't deal with multiple layers of random effects (or as far
as I understand from the information provided.

My questions are:

 - Is it possible to check for outliers considering my data set using
influence.me?
 - If so, how should I specify the command to get what I'm looking
for, and how should I interpret the returned data by the Cook's
distance or dfbetas?
 - If not, is there another package that allows me to check for outliers?

Thanks in advance for your help.
Cheers,


-- 
Maurits

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